EDeepLearningTool represents the common operations of deep learning tools. More...

#include <eVision/EasyDeepLearning/EDeepLearningTool.h>

Inheritance diagram for eVision::EasyDeepLearning::EDeepLearningTool:

Collaboration diagram for eVision::EasyDeepLearning::EDeepLearningTool:

Public Member Functions
virtual	~EDeepLearningTool () throw ()
virtual void	Load (const std::string &filePath)
	Loads a deep learning tool. The given ESerializer must have been created for reading.
virtual void	Load (eVision::ESerializer *serializer)
	Loads a deep learning tool. The given ESerializer must have been created for reading.
std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool >	Clone () const
	Clones this tool.
virtual void	Save (const std::string &filePath, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
virtual void	Save (const std::string &filePath)
	Saves the settings, inference and training model of the deep learning tool.
virtual void	Save (eVision::ESerializer *serializer, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
virtual void	Save (eVision::ESerializer *serializer)
	Saves the settings, inference and training model of the deep learning tool.
virtual void	SerializeSettings (eVision::ESerializer *serializer)=0
	Serializes the tool settings.
void	SerializeInferenceModel (eVision::ESerializer *serializer)
	Serializes the inference model of the tool.
void	SerializeTrainingModel (eVision::ESerializer *serializer)
	Serializes the training model of the tool.
void	SaveSettings (const std::string &filePath)
	Saves the settings of the tool to a file.
void	SaveInferenceModel (const std::string &filePath)
	Saves the tool's inference model to a file.
void	SaveTrainingModel (const std::string &filePath)
	Saves the tool's training model to a file.
void	LoadSettings (const std::string &filePath)
	Loads the settings of a tool.
void	LoadInferenceModel (const std::string &filePath)
	Loads the tool's inference model. The inference model is required to apply the tool on new images.
void	LoadTrainingModel (const std::string &filePath)
	Loads the tool's training model. The training model is required to continue a training.
void	UnloadInferenceModel ()
	Unloads the inference model.
void	UnloadTrainingModel ()
	Unloads the training model.
void	UnloadModels ()
	Unloads the inference and training model.
bool	HasInferenceModel () const
	Whether the inference model is loaded or created when the tool is not yet trained.
bool	HasTrainingModel () const
	Whether the training model is loaded or created when the tool is not yet trained.
virtual eVision::EasyDeepLearning::EDeepLearningToolType	GetToolType () const =0
	Type of the deep learning tool.
bool	IsTrained () const
	Tells whether the deep learning tool has been trained.
void	SetEnableGPU (bool enable)
	Enable the use of a GPU for training and inference of the deep learning tool. If the active engine is not the default engine, EDeepLearningTool::SetEnableGPU will be false even if the active device has a GPU device type. Setting EDeepLearningTool::SetEnableGPU to any value when the active engine is not the default one will throw an exception.
bool	GetEnableGPU () const
	Enable the use of a GPU for training and inference of the deep learning tool. If the active engine is not the default engine, EDeepLearningTool::SetEnableGPU will be false even if the active device has a GPU device type. Setting EDeepLearningTool::SetEnableGPU to any value when the active engine is not the default one will throw an exception.
void	SetGPUIndexes (const std::vector< uint32_t > &indexes)
	Indexes of the GPUs to use for computations.
std::vector< uint32_t >	GetGPUIndexes () const
	Indexes of the GPUs to use for computations.
uint32_t	GetNumGPUs () const
	Number of detected NVIDIA GPU.
void	SetEngine (const std::string &engineName)
	Sets the engine with which to execute or train the neural network.
std::string	GetEngine () const
	Sets the engine with which to execute or train the neural network.
eVision::EasyDeepLearning::EDeepLearningInferencePrecision	GetInferencePrecision () const
	Inference precision.
void	SetInferencePrecision (eVision::EasyDeepLearning::EDeepLearningInferencePrecision prec)
	Inference precision.
int	GetNumAvailableDevices () const
	Available devices for the current EDeepLearningTool::SetEngine.
std::string	GetAvailableDeviceName (int i) const
	Name of the i-th device of the current engine.
eVision::EasyDeepLearning::EDeepLearningDevice	GetAvailableDevice (int i) const
	i-th EDeepLearningDevice of the current engine.
std::vector< eVision::EasyDeepLearning::EDeepLearningDevice >	GetAvailableDevices () const
	Get all available device for the current engine.
int	GetNumActiveDevices () const
	Gets the number of devices with which to run or train the model.
void	SetActiveDevice (const eVision::EasyDeepLearning::EDeepLearningDevice &device)
	Sets the device with which to run or train the model.
eVision::EasyDeepLearning::EDeepLearningDevice	GetActiveDevice (int i) const
	Gets the active device. By default, it returns the first active device.
eVision::EasyDeepLearning::EDeepLearningDevice	GetActiveDevice () const
	Gets the active device. By default, it returns the first active device.
void	SetActiveDeviceByName (const std::string &name)
	Sets the device with which to run the model using its name.
void	SetActiveDeviceById (int id)
	Sets the device with which to run the model using its index in the list returned by EDeepLearningTool::GetAvailableDevices.
std::vector< eVision::EasyDeepLearning::EDeepLearningDevice >	GetActiveDevices () const
	Devices with which to run or train the model.
void	SetActiveDevices (const std::vector< eVision::EasyDeepLearning::EDeepLearningDevice > &devices)
	Devices with which to run or train the model.
void	SetActiveDevicesById (const std::vector< int > &ids)
	Sets the devices with which to run the model using their index in the list returned by EDeepLearningTool::GetAvailableDevices.
eVision::EasyDeepLearning::EDeepLearningExecutionSettings	GetExecutionSettings () const
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
void	SetExecutionSettings (const eVision::EasyDeepLearning::EDeepLearningExecutionSettings &settings)
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
bool	HasEngineSetupData () const
	Whether the inference model has engine setup data.
void	ClearEngineSetupData ()
	Clears the engine data saved in the inference model. Use this method when your engine fails.
eVision::EasyDeepLearning::EDeepLearningDevice	GetEngineSetupDataDevice () const
	Device corresponding to the engine data contained in the tool.
eVision::EasyDeepLearning::EDeepLearningInferencePrecision	GetEngineSetupDataInferencePrecision () const
	Inference precision corresponding to the engine data contained in the tool.
int32_t	GetEngineSetupDataBatchSize () const
	Batch size corresponding to the engine data contained in the tool.
bool	GetOptimizeBatchSize () const
	Indicates whether to optimize the batch size (see EDeepLearningTool::SetBatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
void	SetOptimizeBatchSize (bool optimize)
	Indicates whether to optimize the batch size (see EDeepLearningTool::SetBatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
int32_t	GetBatchSizeForMaximumInferenceSpeed () const
	Computes the batch size that will maximize the inference speed on NVIDIA GPU. For all other devices, it will return 1.
void	SetBatchSize (int32_t size)
	Batch size. The batch size is the number of images that are processed together during training and batch inference.
int32_t	GetBatchSize () const
	Batch size. The batch size is the number of images that are processed together during training and batch inference.
bool	IsTraining () const
	Indicates whether the object is currently training.
int	WaitForTrainingCompletion (int timeout)
	Waits until the training is complete or the timeout is expired. A call to this method will block the calling thread for the shortest time between the timeout and the time it takes for the training to complete.
int	WaitForTrainingCompletion ()
	Waits until the training is complete or the timeout is expired. A call to this method will block the calling thread for the shortest time between the timeout and the time it takes for the training to complete.
int	WaitForIterationCompletion ()
	Waits until an iteration is complete. A call to this method will block the calling thread until a training iteration in the training thread is finished. This method returns the number of trained iterations.
int	StopTraining (bool wait)
	Stops training and returns the last completed iteration. If the parameter 'wait' is set to true, the method will wait for the training thread to completely stop. Otherwise, the method will return immediately.
float	GetCurrentTrainingProgression () const
	Current training progression as a percentage (between 0 and 1).
uint32_t	GetCurrentTrainingFinishedIterations () const
	Number of iterations that are already finished in the current training.
uint32_t	GetCurrentTrainingNumIterations () const
	Total number of training iterations that will be performed during the current training of the deep learning tool.
uint32_t	GetNumTrainedIterations () const
	Number of iterations that were performed to train this deep learning tool.
int	GetBestIteration () const
	Iteration at which the minimum validation error was reached. After training, the tool is in the state it was at this best iteration.
void	SetImageCacheSize (uint64_t size)
	Size in byte of the image cache. The cache is used during training to store reformatted and normalized images. A correctly sized cache can reduce the hard drive accesses and the preprocessing time for each image.
uint64_t	GetImageCacheSize () const
	Size in byte of the image cache. The cache is used during training to store reformatted and normalized images. A correctly sized cache can reduce the hard drive accesses and the preprocessing time for each image.
std::string	GetPath () const
	Path of the tool when loaded or saved using EDeepLearningTool::Save or EDeepLearningTool::Load.
std::string	GetInferenceModelPath () const
	Path of the inference model when loaded or saved using EDeepLearningTool::SaveInferenceModel, EDeepLearningTool::LoadInferenceModel or EDeepLearningTool::SerializeInferenceModel.
std::string	GetTrainingModelPath () const
	Path of the training model when loaded or saved using EDeepLearningTool::SaveTrainingModel, EDeepLearningTool::LoadTrainingModel or EDeepLearningTool::SerializeTrainingModel.
std::string	GetSettingsPath () const
	Path of the settings when loaded or saved using EDeepLearningTool::SaveSettings, EDeepLearningTool::LoadSettings or EDeepLearningTool::SerializeSettings.
void	SetAutoSavePeriod (int period)
	Auto-save period. The auto-save period is the number of training iterations between each automatic save of the model.
int	GetAutoSavePeriod () const
	Auto-save period. The auto-save period is the number of training iterations between each automatic save of the model.
void	SetLearningRate (eVision::EasyDeepLearning::EDeepLearningLearningRateType type, const std::vector< float > &parameters)
	Specifies how the value of the learning rate and how it evolves during training.
eVision::EasyDeepLearning::EDeepLearningLearningRateType	GetLearningRateType () const
	Learning rate type.
std::vector< float >	GetLearningRateParameters () const
	Parameters of the learning rate.
void	SetEnableDeterministicTraining (bool enable)
	Whether to use deterministic training algorithm that allows to repeatable training results. Default value: false.
bool	GetEnableDeterministicTraining () const
	Whether to use deterministic training algorithm that allows to repeatable training results. Default value: false.
void	SetDeterministicTrainingRandomSeed (int32_t seed)
	Random seed to use when EDeepLearningTool::SetEnableDeterministicTraining is set to true. The default value is 42.
int32_t	GetDeterministicTrainingRandomSeed () const
	Random seed to use when EDeepLearningTool::SetEnableDeterministicTraining is set to true. The default value is 42.
void	InitializeInference (int width, int height, eVision::EImageType imageType, int numImages)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (const eVision::EBaseROI &img)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (std::vector< const eVision::EBaseROI * > &imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (std::vector< eVision::EImageBW8 > &imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (std::vector< eVision::EImageBW16 > &imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (std::vector< eVision::EImageC24 > &imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
uint32_t	GetNumLabels () const
	Number of labels of this tool. If the tool is not trained, the method will throw an exception.
std::string	GetLabel (uint32_t index) const
	Gets the label from its index. If the tool is not trained, the method may throw an exception.
virtual void	SetLabel (uint32_t index, const std::string &label)
	Changes a label predicted by the tool.
void	SetLabelWeight (uint32_t index, float weight)
	Sets the label weight. If the tool is not trained, the label weights are not defined. After training, they are set to the value of the training dataset label weights.
float	GetLabelWeight (uint32_t index) const
	Gets the label weight. If the tool is not trained, the label weights are not defined. After training, they are set to the value of the training dataset label weights.
void	SetLabelColor (uint32_t index, const eVision::ERGBColor &c)
	Changes a label color.
eVision::ERGBColor	GetLabelColor (uint32_t index) const
	Label color. The label colors affect how the results will be displayed.
void	SetLabelOpacity (uint32_t index, uint8_t opacity)
	Changes a label opacity (between 0 for fully transparent and 255 for opaque color).
uint8_t	GetLabelOpacity (uint32_t index) const
	Gets the label opacity (between 0 for fully transparent and 255 for opaque color). The label opacities affect with the label colors how the results will be displayed.
int	GetOptimalNumImagesForBatchSize (const eVision::EBaseROI &sampleImage) const
	Optimal number of images to process together for inference given the batch size and EDeepLearningTool::GetNumPatchesForImage.
int	GetOptimalNumImagesForBatchSize (int imageWidth, int imageHeight) const
	Optimal number of images to process together for inference given the batch size and EDeepLearningTool::GetNumPatchesForImage.
int	GetNumPatchesForImage (const eVision::EBaseROI &sampleImage) const
	Number of patches that will be extracted from an input image to perform inference.
virtual int	GetNumPatchesForImage (int imageWidth, int imageHeight) const
	Number of patches that will be extracted from an input image to perform inference.
virtual void	Clean ()
	Reset the engine and clean CPU and GPU memory allocated for running the model.
	EDeepLearningTool (CppWrapperInternal::InternalConstructor)

Static Public Member Functions
static std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool >	Create (const std::string &path)
	Factory method to open a deep learning tool.
static std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool >	Create (eVision::ESerializer *serializer)
	Factory method to open a deep learning tool.
static std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool >	Create (eVision::EasyDeepLearning::EDeepLearningToolType type)
	Factory method to open a deep learning tool.
static int	GetNumAvailableEngines ()
	Number of available engines.
static std::string	GetAvailableEngineName (int id)
	Gets the name of the engine given its id.
static std::vector< std::string >	GetAvailableEngines ()
	Available engines.
static std::vector< eVision::EasyDeepLearning::EDeepLearningDevice >	GetAvailableDevicesForEngine (const std::string &engine)
	Available devices for the specified engine.

Public Attributes
CppWrapperInternal::WrappedClass	wrappedClass_

Detailed Description

EDeepLearningTool represents the common operations of deep learning tools.

The class is responsible for handling CPU/GPU settings and training. Computing the result for an image is called inference and is the responsibility of the actual deep learning tools (see EClassifier).

License(s): EasyClassify or EasySegment or EasyLocate or EasyClassifyInference or EasySegmentInference or EasyLocateInference

Constructor & Destructor Documentation

◆ ~EDeepLearningTool()

eVision::EasyDeepLearning::EDeepLearningTool::~EDeepLearningTool	(		)
throw	(	)

virtual

◆ EDeepLearningTool()

eVision::EasyDeepLearning::EDeepLearningTool::EDeepLearningTool ( CppWrapperInternal::InternalConstructor )

Member Function Documentation

◆ Clean()

void eVision::EasyDeepLearning::EDeepLearningTool::Clean ( )

virtual

Reset the engine and clean CPU and GPU memory allocated for running the model.

This method does not clean all GPU memory. For example, some program data (CUDA kernels) will be kept in GPU memory.

◆ ClearEngineSetupData()

void eVision::EasyDeepLearning::EDeepLearningTool::ClearEngineSetupData ( )

Clears the engine data saved in the inference model. Use this method when your engine fails.

◆ Clone()

std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool > eVision::EasyDeepLearning::EDeepLearningTool::Clone ( ) const

Clones this tool.

◆ Create() [1/3]

std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool > eVision::EasyDeepLearning::EDeepLearningTool::Create ( const std::string & path )

static

Factory method to open a deep learning tool.

Parameters

path	Path to a deep learning tool.

◆ Create() [2/3]

std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool > eVision::EasyDeepLearning::EDeepLearningTool::Create ( eVision::EasyDeepLearning::EDeepLearningToolType type )

static

Factory method to open a deep learning tool.

Parameters

type	Type of tool to create

◆ Create() [3/3]

std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool > eVision::EasyDeepLearning::EDeepLearningTool::Create ( eVision::ESerializer * serializer )

static

Factory method to open a deep learning tool.

Parameters

serializer A serializer created for reading.

◆ GetActiveDevice() [1/2]

eVision::EasyDeepLearning::EDeepLearningDevice eVision::EasyDeepLearning::EDeepLearningTool::GetActiveDevice ( ) const

Gets the active device. By default, it returns the first active device.

◆ GetActiveDevice() [2/2]

eVision::EasyDeepLearning::EDeepLearningDevice eVision::EasyDeepLearning::EDeepLearningTool::GetActiveDevice ( int i ) const

Gets the active device. By default, it returns the first active device.

Parameters

i	Index of the active devices between 0 and EDeepLearningTool::GetNumActiveDevices.

◆ GetActiveDevices()

std::vector< eVision::EasyDeepLearning::EDeepLearningDevice > eVision::EasyDeepLearning::EDeepLearningTool::GetActiveDevices ( ) const

Devices with which to run or train the model.

Only multiple GPUs with the default engine is supported.

◆ GetAutoSavePeriod()

int eVision::EasyDeepLearning::EDeepLearningTool::GetAutoSavePeriod ( ) const

Auto-save period. The auto-save period is the number of training iterations between each automatic save of the model.

In order for the model to be automatically saved during the training, the tool must have been loaded or saved to a file. The path where the tool will be saved is either indicated by EDeepLearningTool::GetPath or the three paths EDeepLearningTool::GetSettingsPath, EDeepLearningTool::GetInferenceModelPath, and EDeepLearningTool::GetTrainingModelPath. If one of these path is empty, the corresponding part of the tool will not be saved automatically during training.

By default, auto save is disabled and the period is equal to 0.

◆ GetAvailableDevice()

eVision::EasyDeepLearning::EDeepLearningDevice eVision::EasyDeepLearning::EDeepLearningTool::GetAvailableDevice ( int i ) const

i-th EDeepLearningDevice of the current engine.

Parameters

i	Index of the device between 0 and EDeepLearningTool::GetNumAvailableDevices - 1.

◆ GetAvailableDeviceName()

std::string eVision::EasyDeepLearning::EDeepLearningTool::GetAvailableDeviceName ( int i ) const

Name of the i-th device of the current engine.

Parameters

i	Index of the device between 0 and EDeepLearningTool::GetNumAvailableDevices - 1.

◆ GetAvailableDevices()

std::vector< eVision::EasyDeepLearning::EDeepLearningDevice > eVision::EasyDeepLearning::EDeepLearningTool::GetAvailableDevices ( ) const

Get all available device for the current engine.

◆ GetAvailableDevicesForEngine()

std::vector< eVision::EasyDeepLearning::EDeepLearningDevice > eVision::EasyDeepLearning::EDeepLearningTool::GetAvailableDevicesForEngine ( const std::string & engine )

static

Available devices for the specified engine.

Parameters

engine Engine for which to get the devices

◆ GetAvailableEngineName()

std::string eVision::EasyDeepLearning::EDeepLearningTool::GetAvailableEngineName ( int id )

static

Gets the name of the engine given its id.

Parameters

id	Index of the available engine between 0 and EDeepLearningTool::GetNumAvailableEngines - 1.

◆ GetAvailableEngines()

std::vector< std::string > eVision::EasyDeepLearning::EDeepLearningTool::GetAvailableEngines ( )

static

Available engines.

◆ GetBatchSize()

int32_t eVision::EasyDeepLearning::EDeepLearningTool::GetBatchSize ( ) const

Batch size. The batch size is the number of images that are processed together during training and batch inference.

When using multi-GPUs processing (see EDeepLearningTool::SetGPUIndexes), the batch size is the number of images that each GPU will process at once.

A large batch size will increase the processing speed on GPU but also the memory requirements.

The batch size must be bigger or equal to 1 and it is commonly chosen to be a power of 2.

If EDeepLearningTool::GetOptimizeBatchSize is 'true', then the value of this property will be optimized automatically for training or inference according to the situation.

◆ GetBatchSizeForMaximumInferenceSpeed()

int32_t eVision::EasyDeepLearning::EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed ( ) const

Computes the batch size that will maximize the inference speed on NVIDIA GPU. For all other devices, it will return 1.

This value is given as an indication and should not necessarily be used in practice.

You must choose a tradeoff between the overall inference speed (also called the throughput), which is limited by this value, and the time it takes to compute the result of a whole batch (also called the latency), which is minimized by making inference image per image (i.e. a batch size of 1). The tradeoff depends on your particular application.

Throw EError_DeepLearningToolNotTrained if the tool is not trained.

◆ GetBestIteration()

int eVision::EasyDeepLearning::EDeepLearningTool::GetBestIteration ( ) const

Iteration at which the minimum validation error was reached. After training, the tool is in the state it was at this best iteration.

◆ GetCurrentTrainingFinishedIterations()

uint32_t eVision::EasyDeepLearning::EDeepLearningTool::GetCurrentTrainingFinishedIterations ( ) const

Number of iterations that are already finished in the current training.

◆ GetCurrentTrainingNumIterations()

uint32_t eVision::EasyDeepLearning::EDeepLearningTool::GetCurrentTrainingNumIterations ( ) const

Total number of training iterations that will be performed during the current training of the deep learning tool.

After the end of the training, this number is the actual number of iterations performed during the training which can be lower than the number of iterations given to the training method (for example if the user called EDeepLearningTool::StopTraining).

◆ GetCurrentTrainingProgression()

float eVision::EasyDeepLearning::EDeepLearningTool::GetCurrentTrainingProgression ( ) const

Current training progression as a percentage (between 0 and 1).

◆ GetDeterministicTrainingRandomSeed()

int32_t eVision::EasyDeepLearning::EDeepLearningTool::GetDeterministicTrainingRandomSeed ( ) const

Random seed to use when EDeepLearningTool::SetEnableDeterministicTraining is set to true. The default value is 42.

When EDeepLearningTool::SetEnableDeterministicTraining, the random seed is generated using a random non-deterministic source.

◆ GetEnableDeterministicTraining()

bool eVision::EasyDeepLearning::EDeepLearningTool::GetEnableDeterministicTraining ( ) const

Whether to use deterministic training algorithm that allows to repeatable training results. Default value: false.

Deterministic training is not guaranteed when:

using multi-core processing is enabled (see Easy::SetMaxNumberOfProcessingThreads) regardless of the value of EDeepLearningTool::SetEnableGPU).
performing several training on the same tool in succession.

◆ GetEnableGPU()

bool eVision::EasyDeepLearning::EDeepLearningTool::GetEnableGPU ( ) const

Enable the use of a GPU for training and inference of the deep learning tool. If the active engine is not the default engine, EDeepLearningTool::SetEnableGPU will be false even if the active device has a GPU device type. Setting EDeepLearningTool::SetEnableGPU to any value when the active engine is not the default one will throw an exception.

Deprecated: Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ GetEngine()

std::string eVision::EasyDeepLearning::EDeepLearningTool::GetEngine ( ) const

Sets the engine with which to execute or train the neural network.

◆ GetEngineSetupDataBatchSize()

int32_t eVision::EasyDeepLearning::EDeepLearningTool::GetEngineSetupDataBatchSize ( ) const

Batch size corresponding to the engine data contained in the tool.

◆ GetEngineSetupDataDevice()

eVision::EasyDeepLearning::EDeepLearningDevice eVision::EasyDeepLearning::EDeepLearningTool::GetEngineSetupDataDevice ( ) const

Device corresponding to the engine data contained in the tool.

◆ GetEngineSetupDataInferencePrecision()

eVision::EasyDeepLearning::EDeepLearningInferencePrecision eVision::EasyDeepLearning::EDeepLearningTool::GetEngineSetupDataInferencePrecision ( ) const

Inference precision corresponding to the engine data contained in the tool.

◆ GetExecutionSettings()

eVision::EasyDeepLearning::EDeepLearningExecutionSettings eVision::EasyDeepLearning::EDeepLearningTool::GetExecutionSettings ( ) const

Execution settings of the tool (engine, device(s), inference precision, batch size settings).

◆ GetGPUIndexes()

std::vector< uint32_t > eVision::EasyDeepLearning::EDeepLearningTool::GetGPUIndexes ( ) const

Indexes of the GPUs to use for computations.

Using multiple GPUs is only possible when we can process multiple images at once, i.e. during training or batch inference.

By default, all the detected GPUs will be used.

The GPU are indexed from 0 to EDeepLearningTool::GetNumGPUs - 1.

Deprecated

Use EDeepLearningTool::GetActiveDevice or EDeepLearningTool::GetActiveDevices.

Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ GetImageCacheSize()

uint64_t eVision::EasyDeepLearning::EDeepLearningTool::GetImageCacheSize ( ) const

Size in byte of the image cache. The cache is used during training to store reformatted and normalized images. A correctly sized cache can reduce the hard drive accesses and the preprocessing time for each image.

◆ GetInferenceModelPath()

std::string eVision::EasyDeepLearning::EDeepLearningTool::GetInferenceModelPath ( ) const

Path of the inference model when loaded or saved using EDeepLearningTool::SaveInferenceModel, EDeepLearningTool::LoadInferenceModel or EDeepLearningTool::SerializeInferenceModel.

◆ GetInferencePrecision()

eVision::EasyDeepLearning::EDeepLearningInferencePrecision eVision::EasyDeepLearning::EDeepLearningTool::GetInferencePrecision ( ) const

Inference precision.

Use EDeepLearningDevice::IsPrecisionSupported to check whether a device support the given precision.

◆ GetLabel()

std::string eVision::EasyDeepLearning::EDeepLearningTool::GetLabel ( uint32_t index ) const

Gets the label from its index. If the tool is not trained, the method may throw an exception.

Parameters

index Index of the label

◆ GetLabelColor()

eVision::ERGBColor eVision::EasyDeepLearning::EDeepLearningTool::GetLabelColor ( uint32_t index ) const

Label color. The label colors affect how the results will be displayed.

The default label colors are the one specified in the dataset used for training.

Parameters

index Index of the label between 0 and EDeepLearningTool::GetNumLabels

◆ GetLabelOpacity()

uint8_t eVision::EasyDeepLearning::EDeepLearningTool::GetLabelOpacity ( uint32_t index ) const

Gets the label opacity (between 0 for fully transparent and 255 for opaque color). The label opacities affect with the label colors how the results will be displayed.

The default label opacities are the one specified in the dataset used for training.

Parameters

index Index of the label between 0 and EDeepLearningTool::GetNumLabels

◆ GetLabelWeight()

float eVision::EasyDeepLearning::EDeepLearningTool::GetLabelWeight ( uint32_t index ) const

Gets the label weight. If the tool is not trained, the label weights are not defined. After training, they are set to the value of the training dataset label weights.

Parameters

index Index of the label

◆ GetLearningRateParameters()

std::vector< float > eVision::EasyDeepLearning::EDeepLearningTool::GetLearningRateParameters ( ) const

Parameters of the learning rate.

◆ GetLearningRateType()

eVision::EasyDeepLearning::EDeepLearningLearningRateType eVision::EasyDeepLearning::EDeepLearningTool::GetLearningRateType ( ) const

Learning rate type.

◆ GetNumActiveDevices()

int eVision::EasyDeepLearning::EDeepLearningTool::GetNumActiveDevices ( ) const

Gets the number of devices with which to run or train the model.

◆ GetNumAvailableDevices()

int eVision::EasyDeepLearning::EDeepLearningTool::GetNumAvailableDevices ( ) const

Available devices for the current EDeepLearningTool::SetEngine.

◆ GetNumAvailableEngines()

int eVision::EasyDeepLearning::EDeepLearningTool::GetNumAvailableEngines ( )

static

Number of available engines.

◆ GetNumGPUs()

uint32_t eVision::EasyDeepLearning::EDeepLearningTool::GetNumGPUs ( ) const

Number of detected NVIDIA GPU.

Deprecated

Use EDeepLearningTool::GetNumAvailableDevices or parse the output of EDeepLearningTool::GetAvailableDevices to find the devices that are GPUs.

Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ GetNumLabels()

uint32_t eVision::EasyDeepLearning::EDeepLearningTool::GetNumLabels ( ) const

Number of labels of this tool. If the tool is not trained, the method will throw an exception.

Some type of tools such as EUnsupervisedSegmenter may have no label at all. For these tools, there is a separate API to control the predicted labels.

◆ GetNumPatchesForImage() [1/2]

int eVision::EasyDeepLearning::EDeepLearningTool::GetNumPatchesForImage ( const eVision::EBaseROI & sampleImage ) const

Number of patches that will be extracted from an input image to perform inference.

For EasyClassify and EasyLocate, this will always be equal to 1. For EasySegment, the number of patches will depend on the scale, patch size and sampling density parameters.

Parameters

sampleImage Image for which to get the number of patch

◆ GetNumPatchesForImage() [2/2]

int eVision::EasyDeepLearning::EDeepLearningTool::GetNumPatchesForImage	(	int	imageWidth,
		int	imageHeight ) const

virtual

Number of patches that will be extracted from an input image to perform inference.

For EasyClassify and EasyLocate, this will always be equal to 1. For EasySegment, the number of patches will depend on the scale, patch size and sampling density parameters.

Parameters

imageWidth	Width of the image for which to get the number of patch
imageHeight	Height of the image for which to get the number of patch

Reimplemented in eVision::EasyDeepLearning::ESupervisedSegmenter, and eVision::EasyDeepLearning::EUnsupervisedSegmenter.

◆ GetNumTrainedIterations()

uint32_t eVision::EasyDeepLearning::EDeepLearningTool::GetNumTrainedIterations ( ) const

Number of iterations that were performed to train this deep learning tool.

This number of iteration may result from the addition of the iterations performed in several calls to the training method.

An iteration can also be called an epoch.

◆ GetOptimalNumImagesForBatchSize() [1/2]

int eVision::EasyDeepLearning::EDeepLearningTool::GetOptimalNumImagesForBatchSize ( const eVision::EBaseROI & sampleImage ) const

Optimal number of images to process together for inference given the batch size and EDeepLearningTool::GetNumPatchesForImage.

In practice, for EasyClassify and EasyLocate, this is the same as the batch size.

For EasySegment, the value will depend on the scale, patch size and sampling density parameters.

Parameters

sampleImage Image for which to get the optimal number of images

◆ GetOptimalNumImagesForBatchSize() [2/2]

int eVision::EasyDeepLearning::EDeepLearningTool::GetOptimalNumImagesForBatchSize	(	int	imageWidth,
		int	imageHeight ) const

Optimal number of images to process together for inference given the batch size and EDeepLearningTool::GetNumPatchesForImage.

In practice, for EasyClassify and EasyLocate, this is the same as the batch size.

For EasySegment, the value will depend on the scale, patch size and sampling density parameters.

Parameters

imageWidth	Width of the image for which to get the optimal number of images
imageHeight	Height of the image for which to get the optimal number of images

◆ GetOptimizeBatchSize()

bool eVision::EasyDeepLearning::EDeepLearningTool::GetOptimizeBatchSize ( ) const

Indicates whether to optimize the batch size (see EDeepLearningTool::SetBatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.

Default value is true.

The batch size will be optimized at the start of training or during EDeepLearningTool::InitializeInference.

◆ GetPath()

std::string eVision::EasyDeepLearning::EDeepLearningTool::GetPath ( ) const

Path of the tool when loaded or saved using EDeepLearningTool::Save or EDeepLearningTool::Load.

◆ GetSettingsPath()

std::string eVision::EasyDeepLearning::EDeepLearningTool::GetSettingsPath ( ) const

Path of the settings when loaded or saved using EDeepLearningTool::SaveSettings, EDeepLearningTool::LoadSettings or EDeepLearningTool::SerializeSettings.

◆ GetToolType()

virtual eVision::EasyDeepLearning::EDeepLearningToolType eVision::EasyDeepLearning::EDeepLearningTool::GetToolType ( ) const

pure virtual

Type of the deep learning tool.

Implemented in eVision::EasyDeepLearning::EClassifier, eVision::EasyDeepLearning::EDeepOCRTool, eVision::EasyDeepLearning::EInterestPointLocator, eVision::EasyDeepLearning::ELocator, eVision::EasyDeepLearning::ESupervisedSegmenter, and eVision::EasyDeepLearning::EUnsupervisedSegmenter.

◆ GetTrainingModelPath()

std::string eVision::EasyDeepLearning::EDeepLearningTool::GetTrainingModelPath ( ) const

Path of the training model when loaded or saved using EDeepLearningTool::SaveTrainingModel, EDeepLearningTool::LoadTrainingModel or EDeepLearningTool::SerializeTrainingModel.

◆ HasEngineSetupData()

bool eVision::EasyDeepLearning::EDeepLearningTool::HasEngineSetupData ( ) const

Whether the inference model has engine setup data.

Engine setup data allows for quicker configuration of the engine when reusing the same device, inference precision and batch size.

Use EDeepLearningTool::GetEngineSetupDataDevice, EDeepLearningTool::GetEngineSetupDataInferencePrecision, and EDeepLearningTool::GetEngineSetupDataBatchSize to retrieve the device (and engine), inference precision, and batch size for which the tool has engine setup data.

The engine setup data is stored in the tool after the first inference or after calling EDeepLearningTool::InitializeInference if the engine supports it. At the moment, only the TensorRT engine supports setup data. If you save the tool, the engine setup data will be saved with the tool and can be reused when loading the tool again.

The engine setup data will only be replaced when using another device and/or inference precision and/or batch size with an engine that supports engine setup data. Otherwise, this setup data will remain (for example, using an engine that does not supports setup data will leave the pervious engine setup data intact).

You can clear the engine setup data by using EDeepLearningTool::ClearEngineSetupData.

◆ HasInferenceModel()

bool eVision::EasyDeepLearning::EDeepLearningTool::HasInferenceModel ( ) const

Whether the inference model is loaded or created when the tool is not yet trained.

◆ HasTrainingModel()

bool eVision::EasyDeepLearning::EDeepLearningTool::HasTrainingModel ( ) const

Whether the training model is loaded or created when the tool is not yet trained.

◆ InitializeInference() [1/6]

void eVision::EasyDeepLearning::EDeepLearningTool::InitializeInference ( const eVision::EBaseROI & img )

Initializes the neural network for inference (i.e. making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If EDeepLearningTool::GetOptimizeBatchSize is equal to true, the value of EDeepLearningTool::SetBatchSize will be recomputed.

Parameters

img	Image for which to prepare the tool for inference

◆ InitializeInference() [2/6]

void eVision::EasyDeepLearning::EDeepLearningTool::InitializeInference	(	int	width,
		int	height,
		eVision::EImageType	imageType,
		int	numImages )

Initializes the neural network for inference (i.e. making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If EDeepLearningTool::GetOptimizeBatchSize is equal to true, the value of EDeepLearningTool::SetBatchSize will be recomputed.

Parameters

width	Width of the images for which to prepare the tool for inference
height	Height of the images for which to prepare the tool for inference
imageType	Type of the images for which to prepare the tool for inference
numImages	Number of images for which to prepare the tool for inference

◆ InitializeInference() [3/6]

void eVision::EasyDeepLearning::EDeepLearningTool::InitializeInference ( std::vector< const eVision::EBaseROI * > & imgList )

Initializes the neural network for inference (i.e. making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If EDeepLearningTool::GetOptimizeBatchSize is equal to true, the value of EDeepLearningTool::SetBatchSize will be recomputed.

Parameters

imgList Vector of images for which to prepare the tool for inference

◆ InitializeInference() [4/6]

void eVision::EasyDeepLearning::EDeepLearningTool::InitializeInference ( std::vector< eVision::EImageBW16 > & imgList )

Initializes the neural network for inference (i.e. making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If EDeepLearningTool::GetOptimizeBatchSize is equal to true, the value of EDeepLearningTool::SetBatchSize will be recomputed.

Parameters

imgList Vector of images for which to prepare the tool for inference

◆ InitializeInference() [5/6]

void eVision::EasyDeepLearning::EDeepLearningTool::InitializeInference ( std::vector< eVision::EImageBW8 > & imgList )

Initializes the neural network for inference (i.e. making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If EDeepLearningTool::GetOptimizeBatchSize is equal to true, the value of EDeepLearningTool::SetBatchSize will be recomputed.

Parameters

imgList Vector of images for which to prepare the tool for inference

◆ InitializeInference() [6/6]

void eVision::EasyDeepLearning::EDeepLearningTool::InitializeInference ( std::vector< eVision::EImageC24 > & imgList )

Initializes the neural network for inference (i.e. making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If EDeepLearningTool::GetOptimizeBatchSize is equal to true, the value of EDeepLearningTool::SetBatchSize will be recomputed.

Parameters

imgList Vector of images for which to prepare the tool for inference

◆ IsTrained()

bool eVision::EasyDeepLearning::EDeepLearningTool::IsTrained ( ) const

Tells whether the deep learning tool has been trained.

◆ IsTraining()

bool eVision::EasyDeepLearning::EDeepLearningTool::IsTraining ( ) const

Indicates whether the object is currently training.

◆ Load() [1/2]

void eVision::EasyDeepLearning::EDeepLearningTool::Load ( const std::string & filePath )

virtual

Loads a deep learning tool. The given ESerializer must have been created for reading.

Parameters

filePath File path.

◆ Load() [2/2]

void eVision::EasyDeepLearning::EDeepLearningTool::Load ( eVision::ESerializer * serializer )

virtual

Loads a deep learning tool. The given ESerializer must have been created for reading.

Parameters

serializer Pointer to the ESerializer created for reading.

◆ LoadInferenceModel()

void eVision::EasyDeepLearning::EDeepLearningTool::LoadInferenceModel ( const std::string & filePath )

Loads the tool's inference model. The inference model is required to apply the tool on new images.

Parameters

filePath File path.

◆ LoadSettings()

void eVision::EasyDeepLearning::EDeepLearningTool::LoadSettings ( const std::string & filePath )

Loads the settings of a tool.

Parameters

filePath File path.

◆ LoadTrainingModel()

void eVision::EasyDeepLearning::EDeepLearningTool::LoadTrainingModel ( const std::string & filePath )

Loads the tool's training model. The training model is required to continue a training.

Parameters

filePath File path.

◆ Save() [1/4]

void eVision::EasyDeepLearning::EDeepLearningTool::Save ( const std::string & filePath )

virtual

Saves the settings, inference and training model of the deep learning tool.

The given ESerializer must have been created for writing.

Parameters

filePath File path.

◆ Save() [2/4]

void eVision::EasyDeepLearning::EDeepLearningTool::Save	(	const std::string &	filePath,
		bool	includeTrainingModel )

virtual

Saves the settings, inference and training model of the deep learning tool.

The given ESerializer must have been created for writing.

Parameters

filePath	File path.
includeTrainingModel	Whether to save the training model. The training model is required to continue training the tool.

◆ Save() [3/4]

void eVision::EasyDeepLearning::EDeepLearningTool::Save ( eVision::ESerializer * serializer )

virtual

Saves the settings, inference and training model of the deep learning tool.

The given ESerializer must have been created for writing.

Parameters

serializer Pointer to the ESerializer created for writing.

◆ Save() [4/4]

void eVision::EasyDeepLearning::EDeepLearningTool::Save	(	eVision::ESerializer *	serializer,
		bool	includeTrainingModel )

virtual

Saves the settings, inference and training model of the deep learning tool.

The given ESerializer must have been created for writing.

Parameters

serializer	Pointer to the ESerializer created for writing.
includeTrainingModel	Whether to save the training model. The training model is required to continue training the tool.

◆ SaveInferenceModel()

void eVision::EasyDeepLearning::EDeepLearningTool::SaveInferenceModel ( const std::string & filePath )

Saves the tool's inference model to a file.

Parameters

filePath File path.

◆ SaveSettings()

void eVision::EasyDeepLearning::EDeepLearningTool::SaveSettings ( const std::string & filePath )

Saves the settings of the tool to a file.

Parameters

filePath File path.

◆ SaveTrainingModel()

void eVision::EasyDeepLearning::EDeepLearningTool::SaveTrainingModel ( const std::string & filePath )

Saves the tool's training model to a file.

Parameters

filePath File path.

◆ SerializeInferenceModel()

void eVision::EasyDeepLearning::EDeepLearningTool::SerializeInferenceModel ( eVision::ESerializer * serializer )

Serializes the inference model of the tool.

Parameters

serializer Pointer to ESerializer

◆ SerializeSettings()

virtual void eVision::EasyDeepLearning::EDeepLearningTool::SerializeSettings ( eVision::ESerializer * serializer )

pure virtual

Serializes the tool settings.

Parameters

serializer Pointer to ESerializer

Implemented in eVision::EasyDeepLearning::EClassifier, eVision::EasyDeepLearning::EDeepOCRTool, eVision::EasyDeepLearning::EInterestPointLocator, eVision::EasyDeepLearning::ELocator, eVision::EasyDeepLearning::ELocatorBase, eVision::EasyDeepLearning::ESupervisedSegmenter, and eVision::EasyDeepLearning::EUnsupervisedSegmenter.

◆ SerializeTrainingModel()

void eVision::EasyDeepLearning::EDeepLearningTool::SerializeTrainingModel ( eVision::ESerializer * serializer )

Serializes the training model of the tool.

The training model is necessary to continue training the tool.

Parameters

serializer Pointer to ESerializer

◆ SetActiveDevice()

void eVision::EasyDeepLearning::EDeepLearningTool::SetActiveDevice ( const eVision::EasyDeepLearning::EDeepLearningDevice & device )

Sets the device with which to run or train the model.

Parameters

device The device with which to run or train the model.

For CPU device, the number of thread is initialized from Easy::SetMaxNumberOfProcessingThreads.

Note that this can change EDeepLearningTool::GetInferencePrecision if the previously set value was incompatible with the device.

◆ SetActiveDeviceById()

void eVision::EasyDeepLearning::EDeepLearningTool::SetActiveDeviceById ( int id )

Sets the device with which to run the model using its index in the list returned by EDeepLearningTool::GetAvailableDevices.

◆ SetActiveDeviceByName()

void eVision::EasyDeepLearning::EDeepLearningTool::SetActiveDeviceByName ( const std::string & name )

Sets the device with which to run the model using its name.

◆ SetActiveDevices()

void eVision::EasyDeepLearning::EDeepLearningTool::SetActiveDevices ( const std::vector< eVision::EasyDeepLearning::EDeepLearningDevice > & devices )

Devices with which to run or train the model.

Only multiple GPUs with the default engine is supported.

Parameters

devices Device

◆ SetActiveDevicesById()

void eVision::EasyDeepLearning::EDeepLearningTool::SetActiveDevicesById ( const std::vector< int > & ids )

Sets the devices with which to run the model using their index in the list returned by EDeepLearningTool::GetAvailableDevices.

Parameters

ids	Index of the devices between 0 and EDeepLearningTool::GetNumAvailableDevices - 1

◆ SetAutoSavePeriod()

void eVision::EasyDeepLearning::EDeepLearningTool::SetAutoSavePeriod ( int period )

Auto-save period. The auto-save period is the number of training iterations between each automatic save of the model.

In order for the model to be automatically saved during the training, the tool must have been loaded or saved to a file. The path where the tool will be saved is either indicated by EDeepLearningTool::GetPath or the three paths EDeepLearningTool::GetSettingsPath, EDeepLearningTool::GetInferenceModelPath, and EDeepLearningTool::GetTrainingModelPath. If one of these path is empty, the corresponding part of the tool will not be saved automatically during training.

By default, auto save is disabled and the period is equal to 0.

Parameters

period New auto-save period.

◆ SetBatchSize()

void eVision::EasyDeepLearning::EDeepLearningTool::SetBatchSize ( int32_t size )

Batch size. The batch size is the number of images that are processed together during training and batch inference.

When using multi-GPUs processing (see EDeepLearningTool::SetGPUIndexes), the batch size is the number of images that each GPU will process at once.

A large batch size will increase the processing speed on GPU but also the memory requirements.

The batch size must be bigger or equal to 1 and it is commonly chosen to be a power of 2.

Parameters

size	size of the batch

If EDeepLearningTool::GetOptimizeBatchSize is 'true', then the value of this property will be optimized automatically for training or inference according to the situation.

◆ SetDeterministicTrainingRandomSeed()

void eVision::EasyDeepLearning::EDeepLearningTool::SetDeterministicTrainingRandomSeed ( int32_t seed )

Random seed to use when EDeepLearningTool::SetEnableDeterministicTraining is set to true. The default value is 42.

When EDeepLearningTool::SetEnableDeterministicTraining, the random seed is generated using a random non-deterministic source.

Parameters

seed	Random seed

◆ SetEnableDeterministicTraining()

void eVision::EasyDeepLearning::EDeepLearningTool::SetEnableDeterministicTraining ( bool enable )

Whether to use deterministic training algorithm that allows to repeatable training results. Default value: false.

Deterministic training is not guaranteed when:

using multi-core processing is enabled (see Easy::SetMaxNumberOfProcessingThreads) regardless of the value of EDeepLearningTool::SetEnableGPU).
performing several training on the same tool in succession.

◆ SetEnableGPU()

void eVision::EasyDeepLearning::EDeepLearningTool::SetEnableGPU ( bool enable )

Enable the use of a GPU for training and inference of the deep learning tool. If the active engine is not the default engine, EDeepLearningTool::SetEnableGPU will be false even if the active device has a GPU device type. Setting EDeepLearningTool::SetEnableGPU to any value when the active engine is not the default one will throw an exception.

Deprecated

Use EDeepLearningTool::GetActiveDevice.

Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ SetEngine()

void eVision::EasyDeepLearning::EDeepLearningTool::SetEngine ( const std::string & engineName )

Sets the engine with which to execute or train the neural network.

◆ SetExecutionSettings()

void eVision::EasyDeepLearning::EDeepLearningTool::SetExecutionSettings ( const eVision::EasyDeepLearning::EDeepLearningExecutionSettings & settings )

Execution settings of the tool (engine, device(s), inference precision, batch size settings).

◆ SetGPUIndexes()

void eVision::EasyDeepLearning::EDeepLearningTool::SetGPUIndexes ( const std::vector< uint32_t > & indexes )

Indexes of the GPUs to use for computations.

Using multiple GPUs is only possible when we can process multiple images at once, i.e. during training or batch inference.

By default, all the detected GPUs will be used.

Parameters

indexes Indexes of the GPU to use for computations

The GPU are indexed from 0 to EDeepLearningTool::GetNumGPUs - 1.

Deprecated

Use EDeepLearningTool::GetActiveDevice or EDeepLearningTool::GetActiveDevices.

Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ SetImageCacheSize()

void eVision::EasyDeepLearning::EDeepLearningTool::SetImageCacheSize ( uint64_t size )

Size in byte of the image cache. The cache is used during training to store reformatted and normalized images. A correctly sized cache can reduce the hard drive accesses and the preprocessing time for each image.

◆ SetInferencePrecision()

void eVision::EasyDeepLearning::EDeepLearningTool::SetInferencePrecision ( eVision::EasyDeepLearning::EDeepLearningInferencePrecision prec )

Inference precision.

Use EDeepLearningDevice::IsPrecisionSupported to check whether a device support the given precision.

◆ SetLabel()

void eVision::EasyDeepLearning::EDeepLearningTool::SetLabel	(	uint32_t	index,
		const std::string &	label )

virtual

Changes a label predicted by the tool.

Parameters

index	Index of the label
label	New label

Be careful when changing one of the label of a trained tool. It can create incompatibilities with previous metrics or results computed with this tool or with the dataset used to trained the tool.

Some tools may not use this label API. For example EUnsupervisedSegmenter uses EUnsupervisedSegmenter::SetGoodLabel instead.

◆ SetLabelColor()

void eVision::EasyDeepLearning::EDeepLearningTool::SetLabelColor	(	uint32_t	index,
		const eVision::ERGBColor &	c )

Changes a label color.

Parameters

index	Index of the label between 0 and EDeepLearningTool::GetNumLabels
c	New color for the specified label

◆ SetLabelOpacity()

void eVision::EasyDeepLearning::EDeepLearningTool::SetLabelOpacity	(	uint32_t	index,
		uint8_t	opacity )

Changes a label opacity (between 0 for fully transparent and 255 for opaque color).

Parameters

index	Index of the label between 0 and EDeepLearningTool::GetNumLabels
opacity	New opacity for the specified label

◆ SetLabelWeight()

void eVision::EasyDeepLearning::EDeepLearningTool::SetLabelWeight	(	uint32_t	index,
		float	weight )

Sets the label weight. If the tool is not trained, the label weights are not defined. After training, they are set to the value of the training dataset label weights.

Parameters

index	Index of the label
weight	Weight

◆ SetLearningRate()

void eVision::EasyDeepLearning::EDeepLearningTool::SetLearningRate	(	eVision::EasyDeepLearning::EDeepLearningLearningRateType	type,
		const std::vector< float > &	parameters )

Specifies how the value of the learning rate and how it evolves during training.

◆ SetOptimizeBatchSize()

void eVision::EasyDeepLearning::EDeepLearningTool::SetOptimizeBatchSize ( bool optimize )

Indicates whether to optimize the batch size (see EDeepLearningTool::SetBatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.

Default value is true.

The batch size will be optimized at the start of training or during EDeepLearningTool::InitializeInference.

◆ StopTraining()

int eVision::EasyDeepLearning::EDeepLearningTool::StopTraining ( bool wait )

Stops training and returns the last completed iteration. If the parameter 'wait' is set to true, the method will wait for the training thread to completely stop. Otherwise, the method will return immediately.

Parameters

wait	Whether to wait for the training to completely stop

◆ UnloadInferenceModel()

void eVision::EasyDeepLearning::EDeepLearningTool::UnloadInferenceModel ( )

Unloads the inference model.

◆ UnloadModels()

void eVision::EasyDeepLearning::EDeepLearningTool::UnloadModels ( )

Unloads the inference and training model.

◆ UnloadTrainingModel()

void eVision::EasyDeepLearning::EDeepLearningTool::UnloadTrainingModel ( )

Unloads the training model.

◆ WaitForIterationCompletion()

int eVision::EasyDeepLearning::EDeepLearningTool::WaitForIterationCompletion ( )

Waits until an iteration is complete. A call to this method will block the calling thread until a training iteration in the training thread is finished. This method returns the number of trained iterations.

◆ WaitForTrainingCompletion() [1/2]

int eVision::EasyDeepLearning::EDeepLearningTool::WaitForTrainingCompletion ( )

Waits until the training is complete or the timeout is expired. A call to this method will block the calling thread for the shortest time between the timeout and the time it takes for the training to complete.

A negative timeout means that the method will wait until the training is complete.

The default value is set to -1.

The method returns the number of trained iterations.

◆ WaitForTrainingCompletion() [2/2]

int eVision::EasyDeepLearning::EDeepLearningTool::WaitForTrainingCompletion ( int timeout )

Waits until the training is complete or the timeout is expired. A call to this method will block the calling thread for the shortest time between the timeout and the time it takes for the training to complete.

A negative timeout means that the method will wait until the training is complete.

The default value is set to -1.

The method returns the number of trained iterations.

Parameters

timeout Timeout in second

Member Data Documentation

◆ wrappedClass_

CppWrapperInternal::WrappedClass eVision::EasyDeepLearning::EDeepLearningTool::wrappedClass_

Public Member Functions

Static Public Member Functions

Public Attributes

Detailed Description

Constructor & Destructor Documentation

◆ ~EDeepLearningTool()

◆ EDeepLearningTool()

Member Function Documentation

◆ Clean()

◆ ClearEngineSetupData()

◆ Clone()

◆ Create() [1/3]

◆ Create() [2/3]

◆ Create() [3/3]

◆ GetActiveDevice() [1/2]

◆ GetActiveDevice() [2/2]

◆ GetActiveDevices()

◆ GetAutoSavePeriod()

◆ GetAvailableDevice()

◆ GetAvailableDeviceName()

◆ GetAvailableDevices()

◆ GetAvailableDevicesForEngine()

◆ GetAvailableEngineName()

◆ GetAvailableEngines()

◆ GetBatchSize()

◆ GetBatchSizeForMaximumInferenceSpeed()

◆ GetBestIteration()

◆ GetCurrentTrainingFinishedIterations()

◆ GetCurrentTrainingNumIterations()

◆ GetCurrentTrainingProgression()

◆ GetDeterministicTrainingRandomSeed()

◆ GetEnableDeterministicTraining()

◆ GetEnableGPU()

◆ GetEngine()

◆ GetEngineSetupDataBatchSize()

◆ GetEngineSetupDataDevice()

◆ GetEngineSetupDataInferencePrecision()

◆ GetExecutionSettings()

◆ GetGPUIndexes()

◆ GetImageCacheSize()

◆ GetInferenceModelPath()

◆ GetInferencePrecision()

◆ GetLabel()

◆ GetLabelColor()

◆ GetLabelOpacity()

◆ GetLabelWeight()

◆ GetLearningRateParameters()

◆ GetLearningRateType()

◆ GetNumActiveDevices()

◆ GetNumAvailableDevices()

◆ GetNumAvailableEngines()

◆ GetNumGPUs()

◆ GetNumLabels()

◆ GetNumPatchesForImage() [1/2]

◆ GetNumPatchesForImage() [2/2]

◆ GetNumTrainedIterations()

◆ GetOptimalNumImagesForBatchSize() [1/2]

◆ GetOptimalNumImagesForBatchSize() [2/2]

◆ GetOptimizeBatchSize()

◆ GetPath()

◆ GetSettingsPath()

◆ GetToolType()

◆ GetTrainingModelPath()

◆ HasEngineSetupData()

◆ HasInferenceModel()

◆ HasTrainingModel()

◆ InitializeInference() [1/6]

◆ InitializeInference() [2/6]

◆ InitializeInference() [3/6]

◆ InitializeInference() [4/6]

◆ InitializeInference() [5/6]

◆ InitializeInference() [6/6]

◆ IsTrained()

◆ IsTraining()

◆ Load() [1/2]

◆ Load() [2/2]

◆ LoadInferenceModel()

◆ LoadSettings()

◆ LoadTrainingModel()

◆ Save() [1/4]